BHFT is a proprietary algorithmic trading firm. Our team manages the full trading cycle, from software development to creating and coding strategies and algorithms.

Our trading operations cover key exchanges. The firm trades across a broad range of asset classes, including equities, equity derivatives, options, commodity futures, rates futures, etc. We employ a diverse and growing array of algorithmic trading strategies, utilizing both High-Frequency Trading (HFT) and Medium-Frequency Trading (MFT) approaches. Looking ahead, we are expanding into new markets and products. As a dynamic company, we continuously experiment with new markets, tools, and technologies.

We’ve got a team of 200+ professionals, with a strong emphasis on technology - 70% are technical specialists in development, infrastructure, testing, and analytics spheres. The remaining part of the team supports our business operations, such as Risks, Compliance, Legal, Operations, and more.

Our employees are located all around the world, from the United States to Hong Kong. Although we maintain office spaces, we currently operate as a 100% remote organization.

At BHFT, clarity and transparency are at the core of our culture: we value open communication, ensuring that our processes are straightforward.

We’re looking for a Quant Researcher with hands-on experience applying volatility models in live trading in TradFi markets. 

We expect the candidate to:

• Have practical experience calibrating volatility surfaces on real market data
  • Including handling gaps, latency issues and so on to effectively use realistic data available in the market
  • MFT’ish research is must. HFT is nice to have.
• Understand how to enforce smoothness, arbitrage-free conditions, and temporal stability
• Be able to tune and debug models under realistic market conditions – including bid/ask spreads, noise, and incomplete markets
• Design and implement logic for position-driven dynamic surface shaping, including:
  • How current portfolio Greeks (vega, gamma, skew) should influence surface parameters such as skew, curvature, and wing behavior
  • Hands-on experience is required for dynamically adapting surface shape based on current exposure
• Ability to identify, model, and mitigate residual noise in implied volatility surfaces, especially:
  • near expiry,
  • around illiquid strikes,
  • or in event-driven conditions.

• Python (mandatory), with strong use of NumPy, pandas, matplotlib, SciPy, and relevant optimization/ML libraries
• Familiarity with standard quant libraries (QuantLib, or custom volatility tools)
• PyTorch / TensorFlow experience (strongly preferred)
• Experience with NSE options and/or other TradFi derivatives with margin impact is a major plus.

Nice to Have:

• Familiarity with practical heuristics for surface management
• Working (not just academic) experience applying ML/DL models (e.g., PyTorch, TensorFlow) to this problem
• Understanding of model explainability and risk of overfitting in execution-sensitive environments
• Direct experience in spot/futures vs. options arbitrage

What we offer:

• Experience a modern international technology company without the burden of bureaucracy.

• Collaborate with industry-leading professionals, including former employees of Tower, DRW, Broadridge, Credit Suisse, and more.

• Enjoy excellent opportunities for professional growth and self-realization.

• Work remotely from anywhere in the world with a flexible schedule.

• Receive compensation for health insurance, sports activities, and non-professional training.